ABSTRACT

As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold.

In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet.

This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.

chapter 1|8 pages

Conjuring Up Dark Matter and Dark Energy

chapter 2|10 pages

Dark Matter in Galaxies

Coming to Grips with an Inevitable Truth

chapter 3|14 pages

Dark Energy Is Fueling a Runaway Universe

chapter 5|54 pages

Methods

Topological Autoencoders for Dynamical Systems in Molecular to Cosmological Applications

chapter 6|22 pages

Querying Artificial Intelligence on Dark Matter and Dark Energy

Quintessential Reverse Engineering of the Standard Model

chapter |6 pages

Epilogue

Conversion of Dark Energy into Dark Matter with Cosmic Reproduction Technology